Bibliographic record
Abstract
What benefits would problem-based learning (PBL) in nursing have to the clinical setting and would clinical instructor input aid in the successful implementation in the clinical setting?The purpose of this study was to research the ways in which PBL could assist in the clinical nursing setting by exploring the views of instructors directly affected by this new process in curriculum delivery at the Southern Alberta Collaborative Nursing Education (SACNE) program.Four sessional clinical instructor interviews were conducted, each reflecting the four clinical concentration areas offered by the SACNE program: (1) Medical/surgical (hospital based), (2) Public/home care (community based), (3) Psychiatry (acute and chronic) and (4) Maternity/pediatrics (hospital based).The participants had a minimum of three years of clinical expertise/experience in the selected area.The interviews were both qualitative and quantitative and were conducted over a four-week period.The data analysis was completed by the end of February, 2002.From the four interviews it was evident the clinical instructors had a basic understanding of PBL but were unsure how to implement it into the clinical setting.Each of the four clinical areas presented with obstacles that might inhibit successful implementation of PBL.Several recommendations were suggested that might aid in the successful implementation ofPBL in the clinical setting.They addressed necessary resources, implementation strategies, learning strategies and stakeholder concerns.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".